Novel insights into wound age estimation: combined with “up, no change, or down” system and cosine similarity in python environment

Dang, Li-hong; Feng, Na; An, Guo-shuai; Cao, Jie; Du, Qiu-xiang; Jin, Qianqian; Huang, Ping; Sun, Junhong

doi:10.1007/s00414-020-02411-z

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…In several preliminary studies of wound age estimation, multibiomarker analysis was suggested to improve wound age estimation sensitivity and specificity [ 1 , 22 , 29 , 30 ]. In the previous studies, multibiomarker analysis was used at morphological level according to the presence or absence of biomarkers [ 9 , 10 ], or at the genetic level according to several biomarkers level changes, such as “up, no change, and down”, to estimate the wound age [ 22 , 30 ]. However, the above data analysis approach forcibly transforms continuous variables into discrete variables, resulting in the loss of valuable information from biomarkers.…”

Section: Discussionmentioning

confidence: 99%

Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy

Cao

et al. 2023

Forensic Sciences Research

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Wound age estimation is one of the most challenging and indispensable issues for forensic pathologists. Although many methods based on physical findings and biochemical tests can be used to estimate wound age, an objective and reliable method for inferring the time interval after injury remains difficult. In present study, endogenous metabolites of contused skeletal muscle were investigated to estimate the time interval after injury. Animal model of skeletal muscle injury was established using Sprague–Dawley rat, and the contused muscles were sampled at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h post-contusion (n = 9). Then, the samples were analyzed using ultra-performance liquid chromatography coupled with high resolution mass spectrometry. A total of 43 differential metabolites in contused muscle were determined by metabolomics method. They were applied to construct a two-level tandem prediction model for wound age estimation based on multilayer perceptron (MLP) algorithm. As a result, all muscle samples were eventually divided into the following subgroups: 4 h, 8 h, 12 h, 16–20 h, 24–32 h, 36–40 h, and 44–48 h. The tandem model exhibited a robust performance and achieved a prediction accuracy of 92.6% which was much higher than that of the single model. In summary, the MLP-MLP tandem machine-learning model based on metabolomics data can be used as a novel strategy for wound age estimation in future forensic casework.

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Section: Discussionmentioning

confidence: 99%

Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy

Cao

et al. 2023

Forensic Sciences Research

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“…The results of Fisher discriminant analysis showed the groups with the highest accuracy at different grouping levels were ARE– group (91.0%), CC group (78.2%), and ARE–CC subgroup (66.7%), respectively ( Table 3 ). In this study, the injury time was strictly determined based on experimental design, and there was no merging of damage time points or stratified discrimination to improve accuracy [ 35 ]. The results showed that using the selected indices to determine the injury time ensures the sensitivity of prediction, simplifies the process of inference, and thus accuracy is improved.…”

Section: Discussionmentioning

confidence: 99%

Screening Criteria of mRNA Indicators for Wound age Estimation

Dong

Liu

et al. 2022

Forensic Sciences Research

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Wound age estimation is a crucial and challenging problem in forensic pathology. Although mRNA is the most commonly used indicator for wound age estimation, screening criteria are lacking. In the present study, the feasibility of screening criteria using mRNA to determine injury time based on the adenylate-uridylate-rich element (ARE) structure and gene ontology (GO) categories were evaluated. A total of 78 Sprague-Dawley male rats were contused and sampled at 4, 8, 12, 16, 20, 24, 28, 32, 36, 40, 44, and 48 h after inflicting injury. The candidate mRNAs were classified based on with or without ARE structure and GO category function. The mRNA expression levels were detected using qRT-PCR. In addition, the standard deviation (STD), mean deviation (MD), relative average deviation (d%), and coefficient of variation (CV) were calculated based on mRNA expression levels. The CV score (CVs) and the CV of CV (CV’CV) were calculated to measure heterogeneity. Finally, based on classic principles, the accuracy of combination of candidate mRNAs was assessed using discriminant analysis to construct a multivariate model for inferring wound age. The results of homogeneity evaluation of each group based on CVs were consistent with the MD, STD, d%, and CV results, indicating the credibility of the evaluation results based on CVs. The candidate mRNAs without ARE structure and classified as cellular component (CC) GO category (ARE–CC) had the highest CVs, showing the mRNAs with these characteristics are the most homogenous mRNAs and best suited for wound age estimation. The highest accuracy was 91.0% when the mRNAs without ARE structure were used to infer the wound age based on the discrimination model. The accuracy of mRNAs classified into CC or multiple function (MF) GO category was higher than mRNAs in the biological process (BP) category. In all subgroups, the accuracy of the composite identification model of mRNA composition without ARE structure and classified as CC was higher than other subgroups. The mRNAs without ARE structure and belonging to the CC GO category were more homogenous, showed higher accuracy for estimating wound age, and were appropriate for rat skeletal muscle wound age estimation. Supplemental data for this article is available online at https://doi.org/10.1080/20961790.2021.1986770 .

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“…The RT-qPCR was performed in a 25 µL reaction system using the Premix Ex Taq Kit (Takara Biotechnology Co., Ltd., Dalian, China) [29]. The contents of the amplification mix and the thermal cycling conditions were set according to the instructions.…”

Section: Relative Quantitative Protocol Of Nine Mrnas' Expressionmentioning

confidence: 99%

Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA

Dang

Bai

et al. 2023

Diagnostics

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(1) Background: Accurate diagnosis of wound age is crucial for investigating violent cases in forensic practice. However, effective biomarkers and forecast methods are lacking. (2) Methods: Samples were collected from rats divided randomly into control and contusion groups at 0, 4, 8, 12, 16, 20, and 24 h post-injury. The characteristics of concern were nine mRNA expression levels. Internal validation data were used to train different machine learning algorithms, namely random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), gradient boosting (GB), and stochastic gradient descent (SGD), to predict wound age. These models were considered the base learners, which were then applied to developing 26 stacking ensemble models combining two, three, four, or five base learners. The best-performing stacking model and base learner were evaluated through external validation data. (3) Results: The best results were obtained using a stacking model of RF + SVM + MLP (accuracy = 92.85%, area under the receiver operating characteristic curve (AUROC) = 0.93, root-mean-square-error (RMSE) = 1.06 h). The wound age prediction performance of the stacking models was also confirmed for another independent dataset. (4) Conclusions: We illustrate that machine learning techniques, especially ensemble algorithms, have a high potential to be used to predict wound age. According to the results, the strategy can be applied to other types of forensic forecasts.

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Novel insights into wound age estimation: combined with “up, no change, or down” system and cosine similarity in python environment

Cited by 8 publications

References 31 publications

Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy

Combined metabolomics and tandem machine-learning models for wound age estimation: a novel analytical strategy

Screening Criteria of mRNA Indicators for Wound age Estimation

Novel Prediction Method Applied to Wound Age Estimation: Developing a Stacking Ensemble Model to Improve Predictive Performance Based on Multi-mRNA

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